Superpower Glass: Delivering Unobtrusive Real-time Social Cues in Wearable Systems
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Abstract (arXiv)
We have developed a system for automatic facial expression recognition, which runs on Google Glass and delivers real-time social cues to the wearer. We evaluate the system as a behavioral aid for children with Autism Spectrum Disorder (ASD), who can greatly benefit from real-time non-invasive emotional cues and are more sensitive to sensory input than neurotypically developing children. In addition, we present a mobile application that enables users of the wearable aid to review their videos along with auto-curated emotional information on the video playback bar. This integrates our learning aid into the context of behavioral therapy. Expanding on our previous work describing in-lab trials, this paper presents our system and application-level design decisions in depth as well as the interface learnings gathered during the use of the system by multiple children with ASD in an at-home iterative trial.
Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). First posted on arXiv 16 Feb 2020.
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Citing papers per year: 2021: 4; 2022: 2; 2023: 5; 2024: 0; 2025: 0; 2026: 1.
12 papers.
- 2021Activity Recognition with Moving Cameras and Few Training Examples: Applications for Detection of Autism-Related Headbangingcited by 15
- 2023A Review of and Roadmap for Data Science and Machine Learning for the Neuropsychiatric Phenotype of Autismcited by 11
- 2021Classification of Abnormal Hand Movement for Aiding in Autism Detection: Machine Learning Studycited by 10
- 2021Training Affective Computer Vision Models by Crowdsourcing Soft-Target Labelscited by 8
- 2021Training and Profiling a Pediatric Emotion Recognition Classifier on Mobile Devicescited by 5
- 2022Challenges and Opportunities for Machine Learning Classification of Behavior and Mental State from Imagescited by 3
- 2023Computer Vision Estimation of Emotion Reaction Intensity in the Wildcited by 2
- 2023Self-Supervised Learning for Audio-Based Emotion Recognitioncited by 1
- 2022An Exploration of Active Learning for Affective Digital Phenotypingcited by 0
- 2023Personalization of Stress Mobile Sensing using Self-Supervised Learningcited by 0
- 2023Personalization of Affective Models to Enable Neuropsychiatric Digital Precision Health Interventions: A Feasibility Studycited by 0
- 2026Cripping AI: Reimagining AI Through Lived Disability Experiencescited by 0
- 2026Cripping AI: Reimagining AI Through Lived Disability Experiences3 May 2026
- 2023Personalization of Affective Models to Enable Neuropsychiatric Digital Precision Health Interventions: A Feasibility Study21 Sep 2023
- 2023Personalization of Stress Mobile Sensing using Self-Supervised Learning4 Aug 2023
- 2023Self-Supervised Learning for Audio-Based Emotion Recognition23 Jul 2023
- 2023Computer Vision Estimation of Emotion Reaction Intensity in the Wild19 Mar 2023
- 2023A Review of and Roadmap for Data Science and Machine Learning for the Neuropsychiatric Phenotype of Autism7 Mar 2023
- 2022An Exploration of Active Learning for Affective Digital Phenotyping5 Apr 2022
- 2022Challenges and Opportunities for Machine Learning Classification of Behavior and Mental State from Images26 Jan 2022
- 2021Training and Profiling a Pediatric Emotion Recognition Classifier on Mobile Devices22 Aug 2021
- 2021Classification of Abnormal Hand Movement for Aiding in Autism Detection: Machine Learning Study18 Aug 2021
- 2021Activity Recognition with Moving Cameras and Few Training Examples: Applications for Detection of Autism-Related Headbanging10 Jan 2021
- 2021Training Affective Computer Vision Models by Crowdsourcing Soft-Target Labels10 Jan 2021